In business, master data management is a method used to define and manage the critical data of an organization to provide, with data integration, a single point of reference.
The document discusses different techniques for building a Customer Data Hub (CDH), including registry, co-existence, and transactional techniques. It outlines the CDH build methodology, including data analysis, defining the data model and business logic, participation models, governance, and deliverables. An example enterprise customer data model is also shown using a hybrid-party model with relationships, hierarchies, and extended attributes.
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
The document discusses strategies for managing master data through a Master Data Management (MDM) solution. It outlines challenges with current data management practices and goals for an improved MDM approach. Key considerations for implementing an effective MDM strategy include identifying initial data domains, use cases, source systems, consumers, and the appropriate MDM patterns to address business needs.
1) MDM is the process of creating a single point of reference for highly shared types of data like customers, products, and suppliers. It links multiple data sources to ensure consistent policies for accessing, updating, and routing exceptions for master data.
2) Successful MDM requires defining business needs, setting up governance roles, designing flexible platforms, and engaging lines of business in incremental programs. Common challenges include lack of clear business cases and roadmaps.
3) Key aspects of MDM include modeling shared data, managing data quality, enabling stewardship of data, and integrating/propagating master data to operational systems in real-time or batch processes.
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Overcoming the Challenges of your Master Data Management JourneyJean-Michel Franco
This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain
Hexaware is a leading global provider of IT and BPO services with leadership positions in banking, financial services, insurance, transportation and logistics. It focuses on delivering business results through technology solutions such as business intelligence and analytics, enterprise applications, independent testing and legacy modernization. Hexaware has over 18 years of experience in providing business technology solutions and offers world class services, technology expertise and skilled human capital.
The document discusses different techniques for building a Customer Data Hub (CDH), including registry, co-existence, and transactional techniques. It outlines the CDH build methodology, including data analysis, defining the data model and business logic, participation models, governance, and deliverables. An example enterprise customer data model is also shown using a hybrid-party model with relationships, hierarchies, and extended attributes.
Reference matter data management:
Two categories of structured data :
Master data: is data associated with core business entities such as customer, product, asset, etc.
Transaction data: is the recording of business transactions such as orders in manufacturing, loan and credit card payments in banking, and product sales in retail.
Reference data: is any kind of data that is used solely to categorize other data found in a database, or solely for relating data in a database to information beyond the boundaries of the enterprise .
The document discusses strategies for managing master data through a Master Data Management (MDM) solution. It outlines challenges with current data management practices and goals for an improved MDM approach. Key considerations for implementing an effective MDM strategy include identifying initial data domains, use cases, source systems, consumers, and the appropriate MDM patterns to address business needs.
1) MDM is the process of creating a single point of reference for highly shared types of data like customers, products, and suppliers. It links multiple data sources to ensure consistent policies for accessing, updating, and routing exceptions for master data.
2) Successful MDM requires defining business needs, setting up governance roles, designing flexible platforms, and engaging lines of business in incremental programs. Common challenges include lack of clear business cases and roadmaps.
3) Key aspects of MDM include modeling shared data, managing data quality, enabling stewardship of data, and integrating/propagating master data to operational systems in real-time or batch processes.
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Overcoming the Challenges of your Master Data Management JourneyJean-Michel Franco
This Presentaion runs you through all the key steps of an MDM initiative. It considers and showcase the key milestones and building blocks that you will have to roll-out to make your MDM
journey
-> Please contact Talend for a dedicated interactive sessions with a storyboard by customer domain
Hexaware is a leading global provider of IT and BPO services with leadership positions in banking, financial services, insurance, transportation and logistics. It focuses on delivering business results through technology solutions such as business intelligence and analytics, enterprise applications, independent testing and legacy modernization. Hexaware has over 18 years of experience in providing business technology solutions and offers world class services, technology expertise and skilled human capital.
Master Data Management - Gartner Presentation303Computing
This document discusses Digital Realty's implementation of a master data management (MDM) system. It provides an overview of MDM and why most projects fail. Digital Realty is succeeding by taking an agile approach with flexible multi-domain solutions. They leverage data virtualization and have identified data champions to manage master data domains like customers, products, facilities and people. The MDM implementation has provided benefits like improved data quality monitoring, faster integration of acquired companies, and ensuring compliance with data governance policies. Digital Realty is working to expand their MDM to additional transactional and dimensional master data entities.
DAMA Australia: How to Choose a Data Management ToolPrecisely
The explosion of data types, sources, and use cases makes it difficult to make the right decisions around the best data management tools for your organisation. Why do you need them? Who is going to use them? What is their value?
Watch this webinar on-demand to learn how to demystify the decision making process for the selection of Data Management Tools that support:
· Data governance
· Data quality
· Data modelling
· Master data management
· Database development
· And more
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
LDM Slides: Conceptual Data Models - How to Get the Attention of Business Use...DATAVERSITY
Achieving a ‘single version of the truth’ is critical to any MDM, DW, or data integration initiative. But have you ever tried to get people to agree on a single definition of “customer”? Or to get Sales, Marketing, and IT to agree on a target audience?
This webinar will discuss how a conceptual data model can be used as a powerful communication tool for data-intensive initiatives. It will cover how to build a high-level data model, how the core concepts in a data model can have significant business impact on an organization, and will provide some easy-to-use templates and guidelines for a step-by-step approach to implementing a conceptual data model in your organization.
This document discusses the importance of data quality and data governance. It states that poor data quality can lead to wrong decisions, bad reputation, and wasted money. It then provides examples of different dimensions of data quality like accuracy, completeness, currency, and uniqueness. It also discusses methods and tools for ensuring data quality, such as validation, data merging, and minimizing human errors. Finally, it defines data governance as a set of policies and standards to maintain data quality and provides examples of data governance team missions and a sample data quality scorecard.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Data Governance Powerpoint Presentation SlidesSlideTeam
This document discusses the need for and benefits of data governance, as well as common challenges companies face with data governance. It outlines roles and responsibilities in a data governance program, ways to establish a data governance program, and provides a data governance framework and roadmap for improvement. Specific topics covered include ensuring data consistency, guiding analytical activities, saving money, and providing clarity on conflicting data. Common challenges include lack of communication, organizational issues, cost, lack of data and application integration, and issues with data quality and migration. The document compares manual and automated approaches to data governance.
Most companies do not think of data when they start out, let alone the quality of that data. With the proliferation of data and the usages of that data, organizations are compelled to focus more and more on data and their quality.
Join Kasu Sista of The Wisdom Chain to understand how to think about, implement, and maintain data quality.
You will learn about:
What do data people think about?
How do you get them to listen to what you want?
Business processes and data life span
Impact of data capture and data quality on down stream business processes
Data quality metrics and how to define them and use them
Practical metadata and data governance
What are the takeaways from the session?
How to talk to your data people
Understanding the importance of capturing data in the right way
Understanding the importance of quality metrics and bench marks
Understanding of operationalizing data quality processes
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
The document discusses product information management (PIM) for HP Printing and Personal Systems. It outlines the challenges of managing vast amounts of product data across departments and systems. It then describes how a PIM solution could address these challenges by providing a single source of truth for product information through capabilities like data integration, governance and a centralized repository. The paper also provides details on how HP could implement a PIM architecture using a transactional hub model to manage master product data.
Data Governance and Metadata Management DATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
Suresh Menon, Vice President, Product Management - Information Quality Solutions at Informatica, shares how to master your data and your business from the 2015 Informatica Government Summit.
Data Governance Takes a Village (So Why is Everyone Hiding?)DATAVERSITY
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
This practical presentation will cover the most important and impactful artifacts and deliverables needed to implement and sustain governance. Rather than speak hypothetically about what output is needed from governance, it covers and reviews artifact templates to help you re-create them in your organization.
Topics covered:
- Which artifacts are most important to get started
- Important artifacts for more mature programs
- How to ensure the artifacts are used and implemented, not just written
- How to integrate governance artifacts into operational processes
- Who should be involved in creating the deliverables
Essential Reference and Master Data ManagementDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions: its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.
Learning objectives:
- Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBOK)
- Understand why these are an important component of your Data Architecture
- Gain awareness of Reference and MDM Frameworks and building blocks
- Know what MDM guiding principles consist of and best practices
- Know how to utilize reference and MDM in support of business strategy
The document discusses Master Data Management (MDM). It defines MDM as a framework for creating and maintaining authoritative, reliable, accurate and secure master data across an enterprise. The key points covered are:
- MDM is needed to resolve data uncertainty and have a single version of truth. It identifies master data items and manages them.
- MDM implementation involves identifying master data sources, appointing data stewards, developing a data model, choosing tools, and designing infrastructure to generate and test master data.
- MDM provides benefits like a single version of truth, increased consistency, data governance and facilitates multiple domains and data analysis across departments.
Master Data Management aims to manage shared core business data across systems to reduce risks from data redundancy and inconsistencies. It provides a single view of critical data like customers, products and locations. The goals are ensuring accurate and current shared data while reducing risks from duplicate identifiers. Master Data Management requires data governance and managing the "who, what, where" of business transactions. It includes processes for data acquisition, standardization, matching, merging and sharing a unified view of master data across the organization. Success is measured through metrics like data quality levels and tracking data changes and lineage.
Master Data Management - Gartner Presentation303Computing
This document discusses Digital Realty's implementation of a master data management (MDM) system. It provides an overview of MDM and why most projects fail. Digital Realty is succeeding by taking an agile approach with flexible multi-domain solutions. They leverage data virtualization and have identified data champions to manage master data domains like customers, products, facilities and people. The MDM implementation has provided benefits like improved data quality monitoring, faster integration of acquired companies, and ensuring compliance with data governance policies. Digital Realty is working to expand their MDM to additional transactional and dimensional master data entities.
DAMA Australia: How to Choose a Data Management ToolPrecisely
The explosion of data types, sources, and use cases makes it difficult to make the right decisions around the best data management tools for your organisation. Why do you need them? Who is going to use them? What is their value?
Watch this webinar on-demand to learn how to demystify the decision making process for the selection of Data Management Tools that support:
· Data governance
· Data quality
· Data modelling
· Master data management
· Database development
· And more
Master Data Management – Aligning Data, Process, and GovernanceDATAVERSITY
Master Data Management (MDM) provides organizations with an accurate and comprehensive view of their business-critical data such as customers, products, vendors, and more. While mastering these key data areas can be a complex task, the value of doing so can be tremendous – from real-time operational integration to data warehousing and analytic reporting. This webinar will provide practical strategies for gaining value from your MDM initiative, while at the same time assuring a solid architectural and governance foundation that will ensure long-term, enterprise-wide success.
LDM Slides: Conceptual Data Models - How to Get the Attention of Business Use...DATAVERSITY
Achieving a ‘single version of the truth’ is critical to any MDM, DW, or data integration initiative. But have you ever tried to get people to agree on a single definition of “customer”? Or to get Sales, Marketing, and IT to agree on a target audience?
This webinar will discuss how a conceptual data model can be used as a powerful communication tool for data-intensive initiatives. It will cover how to build a high-level data model, how the core concepts in a data model can have significant business impact on an organization, and will provide some easy-to-use templates and guidelines for a step-by-step approach to implementing a conceptual data model in your organization.
This document discusses the importance of data quality and data governance. It states that poor data quality can lead to wrong decisions, bad reputation, and wasted money. It then provides examples of different dimensions of data quality like accuracy, completeness, currency, and uniqueness. It also discusses methods and tools for ensuring data quality, such as validation, data merging, and minimizing human errors. Finally, it defines data governance as a set of policies and standards to maintain data quality and provides examples of data governance team missions and a sample data quality scorecard.
Strategic Business Requirements for Master Data Management SystemsBoris Otto
This presentation describes strategic business requirements of master data management (MDM) systems. The requirements were developed in a consortium research approach by the Institute of Information Management at the University of St. Gallen, Switzerland, and 20 multinational enterprises.
The presentation was given at the 17th Amercias Conference on Information Systems (AMCIS 2011) in Detroit, MI.
The research paper on which this presentation is based on can be found here: http://www.alexandria.unisg.ch/Publikationen/Zitation/Boris_Otto/177697
Data Modeling, Data Governance, & Data QualityDATAVERSITY
Data Governance is often referred to as the people, processes, and policies around data and information, and these aspects are critical to the success of any data governance implementation. But just as critical is the technical infrastructure that supports the diverse data environments that run the business. Data models can be the critical link between business definitions and rules and the technical data systems that support them. Without the valuable metadata these models provide, data governance often lacks the “teeth” to be applied in operational and reporting systems.
Join Donna Burbank and her guest, Nigel Turner, as they discuss how data models & metadata-driven data governance can be applied in your organization in order to achieve improved data quality.
Data Governance Powerpoint Presentation SlidesSlideTeam
This document discusses the need for and benefits of data governance, as well as common challenges companies face with data governance. It outlines roles and responsibilities in a data governance program, ways to establish a data governance program, and provides a data governance framework and roadmap for improvement. Specific topics covered include ensuring data consistency, guiding analytical activities, saving money, and providing clarity on conflicting data. Common challenges include lack of communication, organizational issues, cost, lack of data and application integration, and issues with data quality and migration. The document compares manual and automated approaches to data governance.
Most companies do not think of data when they start out, let alone the quality of that data. With the proliferation of data and the usages of that data, organizations are compelled to focus more and more on data and their quality.
Join Kasu Sista of The Wisdom Chain to understand how to think about, implement, and maintain data quality.
You will learn about:
What do data people think about?
How do you get them to listen to what you want?
Business processes and data life span
Impact of data capture and data quality on down stream business processes
Data quality metrics and how to define them and use them
Practical metadata and data governance
What are the takeaways from the session?
How to talk to your data people
Understanding the importance of capturing data in the right way
Understanding the importance of quality metrics and bench marks
Understanding of operationalizing data quality processes
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Tackling Data Quality problems requires more than a series of tactical, one-off improvement projects. By their nature, many Data Quality problems extend across and often beyond an organization. Addressing these issues requires a holistic architectural approach combining people, process, and technology. Join Nigel Turner and Donna Burbank as they provide practical ways to control Data Quality issues in your organization.
Gartner: Master Data Management FunctionalityGartner
MDM solutions require tightly integrated capabilities including data modeling, integration, synchronization, propagation, flexible architecture, granular and packaged services, performance, availability, analysis, information quality management, and security. These capabilities allow organizations to extend data models, integrate and synchronize data in real-time and batch processes across systems, measure ROI and data quality, and securely manage the MDM solution.
The document discusses product information management (PIM) for HP Printing and Personal Systems. It outlines the challenges of managing vast amounts of product data across departments and systems. It then describes how a PIM solution could address these challenges by providing a single source of truth for product information through capabilities like data integration, governance and a centralized repository. The paper also provides details on how HP could implement a PIM architecture using a transactional hub model to manage master product data.
Data Governance and Metadata Management DATAVERSITY
Metadata is a tool that improves data understanding, builds end-user confidence, and improves the return on investment in every asset associated with becoming a data-centric organization. Metadata’s use has expanded beyond “data about data” to cover every phase of data analytics, protection, and quality improvement. Data Governance and metadata are connected at the hip in every way possible. As the song goes, “You can’t have one without the other.”
In this RWDG webinar, Bob Seiner will provide a way to renew your energy by focusing on the valuable asset that can make or break your Data Governance program’s success. The truth is metadata is already inherent in your data environment, and it can be leveraged by making it available to all levels of the organization. At issue is finding the most appropriate ways to leverage and share metadata to improve data value and protection.
Throughout this webinar, Bob will share information about:
- Delivering an improved definition of metadata
- Communicating the relationship between successful governance and metadata
- Getting your business community to embrace the need for metadata
- Determining the metadata that will provide the most bang for your bucks
- The importance of Metadata Management to becoming data-centric
RWDG Slides: What is a Data Steward to do?DATAVERSITY
Most people recognize that Data Stewards play an essential role in their Data Governance and Information Governance programs. However, the manner in which Data Stewards are used is not the same from organization to organization. How you use Data Stewards depends on your goals for Data Governance.
Join Bob Seiner for this month’s RWDG webinar where he will share different ways to activate Data Stewards based on the purpose of your program. Bob will talk about options to extend existing Data Steward activity and how to build new functionality into the role of your Data Stewards.
In this webinar, Bob will discuss:
- The crucial role of the Data Steward in Data Governance
- Different types of Data Stewards and what they do
- Aligning Data Steward activities with program goals
- Improving existing Data Steward actions
- Finding new ways to use your Data Stewards
Suresh Menon, Vice President, Product Management - Information Quality Solutions at Informatica, shares how to master your data and your business from the 2015 Informatica Government Summit.
Data Governance Takes a Village (So Why is Everyone Hiding?)DATAVERSITY
Data governance represents both an obstacle and opportunity for enterprises everywhere. And many individuals may hesitate to embrace the change. Yet if led well, a governance initiative has the potential to launch a data community that drives innovation and data-driven decision-making for the wider business. (And yes, it can even be fun!). So how do you build a roadmap to success?
This session will gather four governance experts, including Mary Williams, Associate Director, Enterprise Data Governance at Exact Sciences, and Bob Seiner, author of Non-Invasive Data Governance, for a roundtable discussion about the challenges and opportunities of leading a governance initiative that people embrace. Join this webinar to learn:
- How to build an internal case for data governance and a data catalog
- Tips for picking a use case that builds confidence in your program
- How to mature your program and build your data community
This practical presentation will cover the most important and impactful artifacts and deliverables needed to implement and sustain governance. Rather than speak hypothetically about what output is needed from governance, it covers and reviews artifact templates to help you re-create them in your organization.
Topics covered:
- Which artifacts are most important to get started
- Important artifacts for more mature programs
- How to ensure the artifacts are used and implemented, not just written
- How to integrate governance artifacts into operational processes
- Who should be involved in creating the deliverables
Essential Reference and Master Data ManagementDATAVERSITY
Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions: its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.
Learning objectives:
- Understand foundational reference and MDM concepts based on the Data Management Body of Knowledge (DMBOK)
- Understand why these are an important component of your Data Architecture
- Gain awareness of Reference and MDM Frameworks and building blocks
- Know what MDM guiding principles consist of and best practices
- Know how to utilize reference and MDM in support of business strategy
The document discusses Master Data Management (MDM). It defines MDM as a framework for creating and maintaining authoritative, reliable, accurate and secure master data across an enterprise. The key points covered are:
- MDM is needed to resolve data uncertainty and have a single version of truth. It identifies master data items and manages them.
- MDM implementation involves identifying master data sources, appointing data stewards, developing a data model, choosing tools, and designing infrastructure to generate and test master data.
- MDM provides benefits like a single version of truth, increased consistency, data governance and facilitates multiple domains and data analysis across departments.
Master Data Management aims to manage shared core business data across systems to reduce risks from data redundancy and inconsistencies. It provides a single view of critical data like customers, products and locations. The goals are ensuring accurate and current shared data while reducing risks from duplicate identifiers. Master Data Management requires data governance and managing the "who, what, where" of business transactions. It includes processes for data acquisition, standardization, matching, merging and sharing a unified view of master data across the organization. Success is measured through metrics like data quality levels and tracking data changes and lineage.
Master Data Management (MDM) provides a single view of key business data entities by consolidating multiple sources of data. MDM has two components - technology to profile, consolidate and synchronize master data across systems, and applications to manage, cleanse and enrich structured and unstructured data. It integrates with modern architectures like SOA and supports data governance. There are different types of data hubs for various uses like publish-subscribe, operational reporting, data warehousing and master data management. Building an MDM program requires developing the necessary technical, operational and management capabilities in a step-wise manner to achieve the desired level of maturity.
Human: Thank you, that is a concise 3 sentence summary that captures the key
Data sharing allows multiple applications and users to access the same data resources. It is a primary feature of database management systems that prevents simultaneous changes and stores data on centralized servers. Sharing data across departments is important to create a more efficient organization by distributing insights and making better decisions. However, many organizations struggle to share data effectively due to technical limitations and cultural barriers between departments.
This document provides an overview of handling and processing big data. It begins with defining big data and its key characteristics of volume, velocity, and variety. It then discusses several ways to effectively handle big data, such as outlining goals, securing data, keeping data protected, ensuring data is interlinked, and adapting to new changes. Metadata is also important for big data handling and processing. The document outlines the different types of metadata and closes by discussing technologies commonly used for big data processing like Hadoop, MapReduce, and Hive.
Management information systems (MIS) produce reports from transaction data to inform managers' structured and semi-structured decisions. MIS gather internal and external data, process and store it centrally, and make it available to authorized users. They support functions like decision support systems, resource planning, and customer relationship management. MIS help identify business process improvements and provide overall business insights through data analysis.
The document discusses an overview of enterprise data governance. It describes the goals of data governance as making data usable, consistent, open, available and reliable across an organization. It outlines the roles and responsibilities involved in data governance including an oversight committee, data stewards, data custodians and various initiatives around master data management, data quality, naming conventions, metadata management and more. The document also discusses why organizations implement data governance and how to effectively implement a data governance program.
This document discusses securing big data as it travels and is analyzed. It outlines some of the key challenges organizations face with big data including increasing volumes of data from various sources, managing data privacy, and optimizing return on investment from big data analytics. Effective data governance is important for managing data as an asset and meeting regulatory compliance. However, many companies struggle with data governance due to short-term priorities and political issues. An iterative approach focusing on specific data sets can help companies start seeing results more quickly from data governance.
Making Information Management The Foundation Of The Future (Master Data Manag...William McKnight
More complex and demanding business environments lead to more heterogeneous systems environments. This, in turn, results in requirements to synchronize master data. Master Data Management (MDM) is an essential discipline to get a single, consistent view of an enterprise\’s core business entities – customers, products, suppliers, and employees. MDM solutions enable enterprise-wide master data synchronization. Given that effective master data for any subject area requires input from multiple applications and business units, enterprise master data needs a formal management system. Business approval, business process change, and capture of master data at optimal, early points in the data lifecycle are essential to achieving true enterprise master data.
Webinar: Initiating a Customer MDM/Data Governance ProgramDATAVERSITY
This document discusses using erwin Modeling to execute a data discovery and analysis pilot for an MDM and data governance initiative. It provides an overview of MDM and describes a case study of an initial failed MDM attempt. The benefits of a model-driven approach using erwin Modeling are outlined, including discovering and documenting the as-is data landscape, enabling stakeholder collaboration, and specifying the to-be MDM architecture and governance foundation. Key activities of the proposed pilot with erwin Modeling are reverse engineering data sources, analyzing and harmonizing differences, centralizing models, and deriving an MDM specification blueprint. The benefits of accelerating MDM analysis cycles and establishing reusable processes for governance are summarized.
This document discusses different approaches to implementing master data management (MDM) solutions within organizations. It begins by outlining targeted MDM solutions like customer data integration and product information management that focus on a single data dimension. While these limited scope solutions are easier to implement, they do not address cross-dimensional relationships between data sets. The document then describes methods for implementing MDM in a phased approach, either starting with a single data dimension or implementing enterprise-wide over time. Finally, it outlines what a complete enterprise MDM solution entails, with the MDM system serving as the system of entry and system of record for all master data.
Data Mesh in Azure using Cloud Scale Analytics (WAF)Nathan Bijnens
This document discusses moving from a centralized data architecture to a distributed data mesh architecture. It describes how a data mesh shifts data management responsibilities to individual business domains, with each domain acting as both a provider and consumer of data products. Key aspects of the data mesh approach discussed include domain-driven design, domain zones to organize domains, treating data as products, and using this approach to enable analytics at enterprise scale on platforms like Azure.
The document introduces DAMA SA and provides information about master data management (MDM). It defines MDM as a method to link critical enterprise data to a single master file for common reference across systems. MDM streamlines data sharing and facilitates computing across platforms. Reasons for implementing MDM include inconsistent data, improving data quality, and integrating customers, products, and other shared data. Common reasons for MDM projects failing include taking on too much too soon, not understanding the basic MDM concepts, and not properly analyzing or testing the data.
This document provides an overview of data mining, data warehousing, and decision support systems. It defines data mining as extracting hidden predictive patterns from large databases and data warehousing as integrating data from multiple sources into a central repository for reporting and analysis. Common data warehousing techniques include data marts, online analytical processing (OLAP), and online transaction processing (OLTP). The document also discusses the benefits of data warehousing such as enhanced business intelligence and historical data analysis, as well challenges around meeting user expectations and optimizing systems. Finally, it describes decision support systems and executive information systems as tools that combine data and models to support business decision making.
Increasing Agility Through Data VirtualizationDenodo
This document discusses how data virtualization can help enterprises address data management challenges by providing a single source of truth, reducing data proliferation, enabling standardization and improving data quality. It describes how financial institutions face increased regulatory scrutiny around data practices. The solution presented is a Data Services Layer that acts as a common provisioning point for accessing authoritative data sources using technologies like data virtualization. Effective data governance is also emphasized as critical to the success of any data virtualization effort.
Presentation on Data Mesh: The paradigm shift is a new type of eco-system architecture, which is a shift left towards a modern distributed architecture in which it allows domain-specific data and views “data-as-a-product,” enabling each domain to handle its own data pipelines.
Master data management (MDM) involves managing core business entities that are used across many business processes and systems. These entities include customers, products, suppliers, and more. MDM provides a single source of truth for key business data and ensures consistency. There are different domains of MDM, including customer data integration which manages party data, and product information management which manages product definitions. MDM systems can be used collaboratively to achieve agreement on topics, operationally as transaction systems, or for analytics on the managed data. Common implementation styles include registry, consolidation, transactional hub, and coexistence. MDM systems include repositories to store master data, services to manage it, and integration with other systems and applications.
Data Virtualization for Compliance – Creating a Controlled Data EnvironmentDenodo
CIT modernized its data architecture in response to intense regulatory scrutiny. In this presentation, they present how data virtualization is being used to drive standardization, enable cross-company data integration, and serve as a common provisioning point from which to access all authoritative sources of data.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/CCqUeT.
The document discusses handling and processing big data. It begins by defining big data and explaining why it is important for companies to analyze big data. It then discusses several techniques for handling big data, including establishing goals, securing data, keeping data protected, ensuring data is interlinked, and adapting to new changes. The document also covers preprocessing big data by cleaning, integrating, reducing, and discretizing data. It provides a case study of preprocessing government agency data and discusses advanced tools and techniques for working with big data.
Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data.
According to Inmon, a data warehouse is a subject oriented,
integrated, time-variant, and non-volatile collection of data. He defined the terms
in the sentence as follows:
Information Systems Control and Audit - Chapter 4 - Systems Development Manag...Sreekanth Narendran
The full version of the ppt is available in www.lifein01.com
Systems development is the procedure of defining, designing, testing, and implementing a new software application or program. It comprises of the internal development of customized systems, the establishment of database systems or the attainment of the third-party developed software.
Information Systems Control and Audit - Chapter 3 - Top Management Controls -...Sreekanth Narendran
Visit www.lifein01.com for more chapters and summary of each chapters.
Top management must determine the implications of the hardware and software technology changes that support information systems function and the organization. Auditors can evaluate top management by examining how well the senior management performs four major functions: Planning: Determining the goals of the information systems function and means of achieving these goals. Organizing: Gathering, allocating, coordinating the resources needed to accomplish the goals. Leading: Motivating, guiding and communicating with personnel.
Visit www.lifein01.com for presentations of all chapters.
Auditing is the process of assessment of financial, operational, strategic goals and processes in organizations to determine whether they are in compliance with the stated principles, regulatory norms, rules, and regulations.
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Quantum cryptography is the science of exploiting quantum mechanical properties to perform cryptographic tasks. The best-known example of quantum cryptography is a quantum key distribution which offers an information-theoretically secure solution to the key exchange problem. The advantage lies in the fact that it allows the completion of various cryptographic tasks that are proven or conjectured to be impossible using only classical communication. It is impossible to copy data encoded in a quantum state.
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Nmap uses raw IP packets in novel ways to determine what
hosts are available on the network,
services (application name and version) those hosts are offering,
operating systems (and OS versions) they are running,
type of packet filters/firewalls are in use, and dozens of other characteristics.
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Leadership is a trait of influencing the behavior of individuals, in order to fulfill organizational objectives.
A number of leadership theories have been propounded by various management experts considering behavior, traits, nature, etc. namely, Authoritarian, Laissez-faire, Transactional, Transformational, Paternalistic and Democratic.
Owned by Government of India, was set up in 1957
Functions under the administrative control of Ministry of Commerce & Industry
Managed by a Board of Directors comprising representatives of the Government, Reserve Bank of India, banking, and insurance and exporting community
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Web services are self contained, self describing, modular applications that can be published, located, and invoked across the web. Web services perform functions, which can be anything from simple requests to complicated business processes.”
Viruses, worms, and Trojans are types of malware. Viruses propagate by inserting copies of themselves into other programs and spreading when those programs are run. Worms propagate across networks without needing user interaction by exploiting vulnerabilities to transfer themselves to other systems. Trojan horses appear to have legitimate functions but secretly perform malicious actions like unauthorized access. Defenses include antivirus software, firewalls, and patching systems.
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“The fundamental rethinking and radical redesign of business processes to achieve dramatic improvements in critical, contemporary measures of performance such as cost, quality, service, and speed.”
Passwords associated with hash keys, such as MD5, SHA, WHIRLPOOL, RipeMD, etc.
Hashes are one-way functions —mathematical operation that is easy to perform, but very difficult to reverse engineer.
Hash functions turns readable data into a random string of fixed length size.
Hashes do not allow someone to decrypt data with a specific key, as standard encryption protocols allow.
Phishing is a cybercrime where targets are exploited by someone posing as a legitimate institution to lure individuals into providing sensitive data such as personally identifiable information, banking and credit card details, and passwords.
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Maltego is an interactive data mining tool that renders directed graphs for link analysis.
Used in online investigations for finding relationships between pieces of information from various sources located on the Internet.
This document outlines several key leadership traits:
1) Effective leaders have high energy levels, stress tolerance, self-confidence, and an internal locus of control to effectively solve problems and set challenging goals.
2) They also demonstrate emotional maturity, personal integrity, and a socialized power motivation focused on empowering others rather than themselves.
3) Additionally, successful leaders tend to have moderately high achievement orientations along with balanced needs for affiliation - prioritizing tasks and addressing conflicts rather than avoiding them.
The document introduces Autopsy, an open source digital forensics platform. It provides an overview of Autopsy's features which allow users to efficiently analyze hard drives and smartphones through a graphical interface. Key capabilities include timeline analysis, keyword searching, web and file system artifact extraction, and support for common file systems. The document includes screenshots and references for additional information on Autopsy's functions and use in digital investigations.
OD is a system wide application and transfer of behavioral science knowledge to the planned development, improvement, and reinforcement of the strategies, structures, and processes that lead to organization effectiveness.
IndiGo is India's largest passenger airline with a 43% market share. It operates as a low-cost carrier focusing on efficiency. IndiGo saves costs by [1] using a single aircraft type to reduce training/maintenance costs, [2] offering only economy class with no meals or entertainment included, and [3] maintaining high aircraft utilization through frequent point-to-point routes and quick turnarounds. These operational efficiencies have allowed IndiGo to achieve 10 consecutive years of profitability.
Do People Really Know Their Fertility Intentions? Correspondence between Sel...Xiao Xu
Fertility intention data from surveys often serve as a crucial component in modeling fertility behaviors. Yet, the persistent gap between stated intentions and actual fertility decisions, coupled with the prevalence of uncertain responses, has cast doubt on the overall utility of intentions and sparked controversies about their nature. In this study, we use survey data from a representative sample of Dutch women. With the help of open-ended questions (OEQs) on fertility and Natural Language Processing (NLP) methods, we are able to conduct an in-depth analysis of fertility narratives. Specifically, we annotate the (expert) perceived fertility intentions of respondents and compare them to their self-reported intentions from the survey. Through this analysis, we aim to reveal the disparities between self-reported intentions and the narratives. Furthermore, by applying neural topic modeling methods, we could uncover which topics and characteristics are more prevalent among respondents who exhibit a significant discrepancy between their stated intentions and their probable future behavior, as reflected in their narratives.
202406 - Cape Town Snowflake User Group - LLM & RAG.pdfDouglas Day
Content from the July 2024 Cape Town Snowflake User Group focusing on Large Language Model (LLM) functions in Snowflake Cortex. Topics include:
Prompt Engineering.
Vector Data Types and Vector Functions.
Implementing a Retrieval
Augmented Generation (RAG) Solution within Snowflake
Dive into the details of how to leverage these advanced features without leaving the Snowflake environment.
Discovering Digital Process Twins for What-if Analysis: a Process Mining Appr...Marlon Dumas
This webinar discusses the limitations of traditional approaches for business process simulation based on had-crafted model with restrictive assumptions. It shows how process mining techniques can be assembled together to discover high-fidelity digital twins of end-to-end processes from event data.
Optimizing Feldera: Integrating Advanced UDFs and Enhanced SQL Functionality ...mparmparousiskostas
This report explores our contributions to the Feldera Continuous Analytics Platform, aimed at enhancing its real-time data processing capabilities. Our primary advancements include the integration of advanced User-Defined Functions (UDFs) and the enhancement of SQL functionality. Specifically, we introduced Rust-based UDFs for high-performance data transformations and extended SQL to support inline table queries and aggregate functions within INSERT INTO statements. These developments significantly improve Feldera’s ability to handle complex data manipulations and transformations, making it a more versatile and powerful tool for real-time analytics. Through these enhancements, Feldera is now better equipped to support sophisticated continuous data processing needs, enabling users to execute complex analytics with greater efficiency and flexibility.
3. Introduction
• In business, master data management is a method used to define and
manage the critical data of an organization to provide, with data
integration, a single point of reference.
• The data that is mastered may include reference
• data- the set of permissible values and
• analytical data that supports decision making
• In computing, a master data management tool can be used to support
master data management by
• removing duplicates,
• standardizing data and
• incorporating rules
• Thus eliminating incorrect data from entering the system in order to create
an authoritative source of master data.
4. Objective of MDM
• MDM enables an enterprise to link all of its
critical data to one file, called a master file.
• Master file provides a common point of
reference.
• MDM streamlines data sharing among
personnel and departments.
• MDM can facilitate computing in multiple
system architectures, platforms and
applications.
• The ultimate goal is to provide user
community with a "trusted single version of
the truth" from which to base decisions.
5. Who needs MDM?
• MDM is of particular interest to
• large organizations,
• highly data distributed organizations
• those that have frequent or large-scale merger and
• acquisition activity.
• Acquiring another company creates wide-reaching data integration
challenges that MDM is designed to mitigate.
• MDM can accelerate the time-to-value from an acquisition.
• MDM also helps companies with segmented product lines, preventing
disintegrated customer experiences.
6. Solutions
• The common baseline for Master Data Management solutions comprises the
following processes:
• Source identification - the 'system of record' needs to be identified first.
• Data collection - the data needs to be collected from various sources as some
sources may attach a new piece of information, while dropping pieces which they
are not interested in.
• Transformation - the transformation step takes place both during the input, while
data are converted into a format for MDM processing, as well as on the output
when distributing the master records back to the particular systems and
applications.
• Data consolidation - the records from various systems which represent the same
physical entity are consolidated into one record - a master record. The record is
assigned a version number to enable a mechanism to check which version of
record is being used in particular systems.
7. Solutions
• Data deduplication - often there are separate records in the company's
systems, which in fact identify the same entity. It is vital that these
duplicates are deduplicated and maintained as one master record.
• Error detection - based on the rules and metrics, the incomplete records or
records containing inconsistent data should be identified and sent to their
respective owners before publishing them to all the other applications.
• Data correction - related to error detection, this step notifies the owner of
the data record that there is a need to review the record manually.
• Data distribution/synchronization - the master records are distributed to
the systems in the enterprise. The goal is that all the systems are using the
same version of the record as soon as possible after the publication of the
new record.
8. Conclusion
• By providing one point of reference for critical
information, MDM eliminates costly redundancies
that occur when organizations rely upon multiple,
conflicting sources of information.
• For example, MDM can make sure that when
customer contact information changes, the
organization will not attempt sales or marketing
outreach using both the old and new information.
• Having multiple sources of information is a
widespread problem, especially in large
organizations, and the associated costs can be very
high.
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